Update README.md

README.md

@@ -38,25 +38,22 @@ Transformer-based approach, the Word2Vec-based approach improved the accuracy ra
 
 ## Model description
 
-After the embedding, which is just essentially data preprocessing, it is necessary to develop the Project 
-further to analyze the input text and classify whether it is a valid research funding opportunity for 
-Brazilian or not. 
-
-For the project, the best option would be chosen empirically upon comparing the results of 4 distinct architectures:
-Neural Network (NN), Deep Neural Network (DNN), Long Short-Term Memory (LSTM), and Convolutional Neural Network (CNN).
-Figure 4 shows the structure of the models.
-
-A neural network (NN) here is a simple feedforward neural network with only a single hidden layer, usually called 
-”shallow”. Shallow NNs are often limited in the complexity of the problems they can be trained to solve well. 
-
-Our CNN model uses a dropout layer feeding into a couple of Conv1D layers and then a MaxPooling layer. After that, 
-we Figure 4: Classification models use a hidden layer composed of a dense layer of size 128, followed by another 
-dropout layer, and finally, the Flatten and final dense classification layer. 
-
-The architecture of the CNN network used is composed of a 50% dropout layer followed by two 1D convolution 
-layers associated with a MaxPooling layer. After max pooling a dense layer of size 128 was added connected 
-to a 50% dropout which finally connects to a flatten layer and the final classification dense layer. Dropout 
-layers help to avoid overfitting the network by masking part of the data so that the network learns to create 
+The work consists of a machine learning model with word embedding and Convolutional Neural Network (CNN).
+For the project, a Convolutional Neural Network (CNN) was chosen, as it presents better accuracy in empirical
+comparison with 3 other different architectures: Neural Network (NN), Deep Neural Network (DNN) and Long-Term 
+Memory (LSTM).
+
+As the input data is compose of unstructured and nonuniform texts it is essential normalize the data to study 
+little insights and valuable relationships to work with the best features of them. In this way, learning is 
+facilitated and allows the gradient descent to converge more quickly.
+
+The first layer of the model is an embedding layer as a method of extracting features from the data that can 
+replace one-hot coding with dimensional reduction.
+
+The architecture of the CNN network is composed of a 50% dropout layer followed by two 1D convolution layers 
+associated with a MaxPooling layer. After maximum grouping, a dense layer of size 128 is added connected to 
+a 50% dropout which finally connects to a flattened layer and the final sort dense layer. The dropout layers 
+helped to avoid network overfitting by masking part of the data so that the network learned to create 
 redundancies in the analysis of the inputs.
 
 ![CNN Classification Model](https://raw.githubusercontent.com/chap0lin/WEBIST2022/master/Assets/cnn_model.png)